Dual-Loop Iterative Learning Control With Application to an Ultraprecision Wafer Stage

Abstract

Iterative learning control (ILC) enables high performance for motion systems executing repetitive tasks. The robustness filter of ILC enhances the robustness w.r.t. model uncertainties and disturbances, but results in that the repetitive error cannot be eliminated. In this article, a dual-loop ILC (DILC) approach is proposed for precision motion systems to explicitly address the design tradeoff of standard ILC between robustness and tracking performance. In the proposed DILC approach, the standard ILC is paralleled with an additional feedforward signal. When ILC converges, the additional feedforward signal is updated by the converged total feedforward signal, and then, the ILC begins a new iteration. As a result, the nonzero asymptotic error caused by the robustness filter is eliminated by adding an iterative action over the feedforward signal onto ILC. Comparative simulation and experimental results confirm that, compared to ILC, the proposed DILC can significantly enhance the tracking performance without the sacrifice of robustness w.r.t. model uncertainties and disturbances. Application to an ultraprecision wafer stage illustrates that the proposed DILC decreases the peak values of moving average and moving standard deviation of the tracking error by 52.7% and 43.9%, respectively.